mHealth to Early Detect Exacerbation for Older People With Heart Failure (mobileHF)

(in Spanish) Utilidad de un Instrumento telediagnóstico en la detección de la reagudización de la Insuficiencia Cardiaca crónica en Ancianos

The aim of the study was to demonstrate the effectiveness of telemonitoring functional status and vital signs to early detect heart failure exacerbation, and to minimize readmissions and length of hospitalisations.

Patients over 75 with heart failure were included after a hospitalisation due to heart failure exacerbation. Patients were assigned randomly into intervention or control. The intervention group comprised 47 patients who were assessed through telemonitoring, while 40 followed traditional clinical pathways.

Patients were followed-up for 3 months after discharge, collecting emergency visits and readmissions due to a new heart failure exacerbation. Those patients in the intervention group used a commercial telemedicine system: Careline H@me, which was provided by the company SALUDNOVA. They system was personalized by adding the functional status monitoring capabilities. Thus, the system collected vital signs (i.e. blood pressure, heart rate, respiratory rate, oxygen saturation, glucose, and weight); symptoms of decompensated heart failure (i.e. dyspnoea and orthopnoea); and functional status (i.e. part of the Short Physical Performance Battery, SPPB) and a brief questionnaire (i.e. Do you have the medication?) every 48 hours.

Two staff geriatricians at the Hospital Universitario de Getafe accessed, during working days, a secured dedicated web-portal to assess the progression of their patients, evaluated through the monitored variables and the self reported symptoms. They reacted to the data, if needed, making a phone call or visiting the patient. Besides, patients could contact the geriatricians or attending the emergency room as usually.

After completion, we analysed the results. First, we carried out a descriptive analysis of the data. Later, we analysed the effects of the intervention with telemedicine and the predictive values of the different measured variables through logistic regressions.

Study Overview

• Status: Completed
• Conditions: Heart Failure
• Intervention / Treatment: Device: Intervention using mHealth

Detailed Description

Patient registries were carried out in two different Databases: one included general information on patients and epidemiological data. The other database stored the monitored data obtained every 48 hours.

Quality assurance and source data verification was achieved by retrieving general information on patients from the Hospital Electronic Medical Record. Moreover epidemiological information was assessed by the Principal Investigator who removed outlier values and assessed the occurrence of sharp changes in temporal series (e.g. changes >0.5Kg in two days).

Patients were recruited at the acute unit of the Geriatrics Service. When they were admitted due a heart failure exacerbation a trained nurse performed the initial assess. If the patient was suitable, a geriatrician explained to him /her the study and if the patient accepted we was asked to sign the informed consent. Then he was assigned randomly to a group. For those in the intervention group, they were trained by the geriatrician and the nurse on how to perform the measurements in the mobile phone. For those in the control group, the staff recorded the baseline characteristics, and during the follow-up the emergency visits, readmissions and death was recorded. These latter variables were also recorded in the patients of the intervention group.

The data dictionary included variables identifying patients and others describing their status and evolution:

• Oxygen saturation, measured as an integer (80-100)
• Heart rate, measured in beats/minute (0-200)
• Diastolic blood, measured in mmHg (0-100)
• Systolic blood pressure, measured in mmHg (0-100)
• Respiratory rate, measured in breaths/minute (0-100)
• Skin temperature, measured in Celsius
• Weight, measured in kg
• Glucose, measured in mg/dl (0-500)
• Short Physical Performance Battery, time to walk for 4.3 meters, measured in seconds (0-100)
• Short Physical Performance Battery, gait speed for 4.3 meters, measured in decimal meters/second (0-1)
• Short Physical Performance Battery, chair stand test once, measured in seconds (0-100)
• Symptom question Q1: Do you feel worse than the last time?, measured as a Boolean variable (YES/NO)
• Symptom question Q2: Do you feel worse than the day of the hospital discharge?, measured as a Boolean variable (YES/NO)
• Symptom question Q3: Do you feel shorten of breath?, measured as a categorical variable (effort/DLA/at rest)
• Symptom question Q4: Do you feel your legs swollen? ?, measured as a Boolean variable (YES/NO)
• Symptom question Q5: Have you woken up during the night feeling fatigue? ?, measured as a Boolean variable (YES/NO)
• Symptom question Q6: Have you needed more pillows to sleep than last time? ?, measured as a Boolean variable (YES/NO)
• Symptom question Q7: Have you taken all your drugs? ?, measured as a Boolean variable (YES/NO)
• Symptom question Q8: Have you followed the food and drinks indications of your physician?, measured as a Boolean variable (YES/NO) Sample size was calculated to reduce readmission rate by 10% in 3 months time. Having a significant level of 0.05. Sample: 50-60 per group. With the first 20 patients included, sample size was recalculated, only 40 were needed in each group. However, we included more patients in the intervention (up to 50) to face drops, only 3 dropped the study, so we obtained data from 47 in the intervention group.

Missing data were excluded from the analysis, the registry was excluded. If a patient registered more than one measurement for the same expected data, the latest remained, and the rest was deleted.

Statistical analysis:

First, we carried out a descriptive analysis of the data. Later, we analysed the effects of the intervention with telemedicine and the predictive values of the different measured variables through logistic regressions.

Descriptive data are presented as median values (IQR). Baseline characteristics between control and intervention groups were compared using non-parametric tests (Mann-Whitney).

In order to determine the effects of the intervention, the analysis was divided in two parts. First, a logistic regression model was fitted to determine if the patient monitorization (yes/no) significantly predict worsening using age and sex as covariates. Then, a new variable, hospitalization length of stay, was created with the length of stay for any readmission in each group to assess whether the use of telemonitoring affected the length of hospitalisation. Both control and intervention groups were compared using Mann-Whitney.

Finally, in the intervention group, the same logistic regression model was fitted to determine the factors that significantly predict worsening.

All analyses were made with the Statistical Package R for windows (Vienna, Austria) (http://www.r-project.org), version 3.1.1. P-value was set at level <0.05.

• Study Type: Interventional
• Enrollment (Actual): 90
• Phase: Not Applicable

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 75 years and older (OLDER_ADULT)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

Description

Inclusion Criteria:

• Patients admitted at the acute care unit at the Hospital Universitario de Getafe who are diagnosed with Heart Failure as primary cause for admission.

Exclusion Criteria:

• Moderate/severe cognitive impairment (MMSE<23)
• Visual/auditory deficits, communication problems
• Moderate/severe non-reversible functional impairment (Barthel<60)
• To be institutionalized
• To be discharged to a Functional Rehabilitation Hospital

Study Plan

How is the study designed?

Design Details

• Primary Purpose: SUPPORTIVE_CARE
• Allocation: RANDOMIZED
• Interventional Model: PARALLEL
• Masking: NONE

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
ACTIVE_COMPARATOR: Intervention using mHealth; Patients in the intervention used at home a mobile system to monitor their clinical and health status.	Device: Intervention using mHealth; Patients in the intervention used at home a mobile system to monitor their clinical and health status.
NO_INTERVENTION: Control; Patients in the control group received the usual care.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Readmissions	Number of visits to the emergency room that require readmission	3 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Emergency visits	Number of visits to the emergency room that did not require readmission	3 months
Worsening	Sum of emergency visits + readmissions, used as proxy of worsening	3 months
Readmissions length	Length of hospitalizations in days	3 months

Collaborators and Investigators

• Sponsor: Hospital Universitario Getafe
• Collaborators: Instituto de Salud Carlos III; SALUDNOVA SOLUTION S.L

Study record dates

Study Major Dates

• Study Start: August 1, 2010
• Primary Completion (ACTUAL): October 1, 2013
• Study Completion (ACTUAL): October 1, 2013

Study Registration Dates

• First Submitted: July 22, 2015
• First Submitted That Met QC Criteria: July 22, 2015
• First Posted (ESTIMATE): July 23, 2015

Study Record Updates

• Last Update Posted (ESTIMATE): July 23, 2015
• Last Update Submitted That Met QC Criteria: July 22, 2015
• Last Verified: July 1, 2015

More Information

Terms related to this study

• Keywords: mHealth; functional status; telemonitoring; older people
• Additional Relevant MeSH Terms: Heart Diseases; Cardiovascular Diseases; Heart Failure
• Other Study ID Numbers: HUGetafe; PI09/91070 (OTHER_GRANT: Instituto de Salud Carlos III, SPAIN); CEIC-09/37 (OTHER: Ethical Committee at the Hospital Universitario de Getafe, SPAIN)